Visible light induced step-growth polymerization by electrophilic aromatic substitution reactions

2021 ◽  
Author(s):  
Huseyin Cem Kiliclar ◽  
Cagatay Altinkok ◽  
Gorkem Yilmaz ◽  
Yusuf Yagci
Keyword(s):  
Visible Light ◽  
Electrophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Substitution Reactions ◽  
Step Growth ◽  
Step Growth Polymerization

A novel visible light induced step-growth polymerization to form poly(phenylene methylene) by electrophilic aromatic substitution reactions is described.

Unveiling the Regioselectivity in Electrophilic Aromatic Substitution Reactions of Deactivated Benzenes through Molecular Electron Density Theory

2021 ◽  
Author(s):  
Luis R. Domingo ◽  
Mar Ríos-Gutiérrez ◽  
María José Aurell
Keyword(s):  
Electron Density ◽  
Electrophilic Aromatic Substitution ◽  
Benzene Derivatives ◽  
Aromatic Substitution ◽  
Substitution Reactions ◽  
Molecular Electron ◽  
Molecular Electron Density Theory

The origin of the meta regioselectivity in electrophilic aromatic substitution (EAS) reactions of deactivated benzene derivatives is herein analysed through Molecular Electron Density Theory (MEDT). To this end, the EAS...

scholarly journals RegioML: Predicting the regioselectivity of electrophilic aromatic substitution reactions using machine learning

2021 ◽  
Author(s):  
Nicolai Ree ◽  
Andreas H. Göller ◽  
Jan H. Jensen
Keyword(s):  
Machine Learning ◽  
Tight Binding ◽  
Reaction Centers ◽  
Gradient Boosting ◽  
Electrophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Substitution Reactions ◽  
Test Set ◽  
Light Gradient ◽  
Out Of Sample

We present RegioML, an atom-based machine learning model for predicting the regioselectivities of electrophilic aromatic substitution reactions. The model relies on CM5 atomic charges computed using semiempirical tight binding (GFN1-xTB) combined with the ensemble decision tree variant light gradient boosting machine (LightGBM). The model is trained and tested on 21,201 bromination reactions with 101K reaction centers, which is split into a training, test, and out-of-sample datasets with 58K, 15K, and 27K reaction centers, respectively. The accuracy is 93% for the test set and 90% for the out-of-sample set, while the precision (the percentage of positive predictions that are correct) is 88% and 80%, respectively. The test-set performance is very similar to the graph-based WLN method developed by Struble et al. (React. Chem. Eng. 2020, 5, 896) though the comparison is complicated by the possibility that some of the test and out-of-sample molecules are used to train WLN. RegioML out-performs our physics-based RegioSQM20 method (J. Cheminform. 2021, 13:10) where the precision is only 75%. Even for the out-of-sample dataset, RegioML slightly outperforms RegioSQM20. The good performance of RegioML and WLN is in large part due to the large datasets available for this type of reaction. However, for reactions where there is little experimental data, physics-based approaches like RegioSQM20 can be used to generate synthetic data for model training. We demonstrate this by showing that the performance of RegioSQM20 can be reproduced by a ML-model trained on RegioSQM20-generated data.

Homoazulene electrophilic aromatic substitution reactions. Parallels to the chemistry of azulene

1989 ◽  
Vol 30 (3) ◽  
pp. 305-308 ◽  
Author(s):  
Lawrence T. Scott ◽  
Chris A. Sumpter ◽  
Mitsunori Oda ◽  
Ihsan Erden
Keyword(s):  
Electrophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Substitution Reactions
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